One ongoing field of computer vision research is human face detection. Human face detection involves automatically detecting human faces and facial characteristics, such as skin color, in digital photographic images.
Various methods of face detection have been developed to automatically detect the presence of a human face in a digital photographic image. Some learning-based face detection methods such as those using neural networks and AdaBoost techniques usually work with gray-scale images. Thus, given a color input image, these face detection methods first convert the color input image to a gray-scale image before attempting to detect a human face in the gray-scale image.
For example, a color digital image I with the pixels I(x, y) can be arranged in a K1×K2 pixel array. The term (x, y) denotes the spatial location where x=1, 2, . . . , K1 and y=1, 2, . . . , K2 indicate the image row and column, respectively. Each pixel I(x, y) represents the color vector I(x, y)=[R(x, y), G(x, y), B(x, y)] in which R(x, y), G(x, y), and B(x, y) denote R (red), G (green), and B (blue) color channels, respectively, of the image I. The input color image I can be converted to the output gray-scale image J using the following equation:J(x,y)=α·R(x,y)+β·G(x,y)+γ·B(x,y)  [Equation 1]where α, β, and γ are the weights corresponding to the three color channels R, G, and B.
The most popular values for the weights α, β, and γ in [Equation 1] use the luminance values of the so-called NTSC color space from standard NTSC conversion formula, which are α=0.299, β=0.587, and γ=0.114. These popular values are optimized for displaying gray-scale pictures on monochrome (black and white) televisions. However, these popular values are not optimized for certain automated imaging processes, such as certain face detection processes.